Overview

Dataset statistics

Number of variables35
Number of observations336442
Missing cells0
Missing cells (%)0.0%
Duplicate rows167
Duplicate rows (%)< 0.1%
Total size in memory89.8 MiB
Average record size in memory280.0 B

Variable types

Numeric13
Categorical22

Alerts

Dataset has 167 (< 0.1%) duplicate rowsDuplicates
category is highly overall correlated with category_4 and 1 other fieldsHigh correlation
main_category is highly overall correlated with main_category_0 and 3 other fieldsHigh correlation
goal is highly overall correlated with goal_usdHigh correlation
pledged is highly overall correlated with backers and 1 other fieldsHigh correlation
backers is highly overall correlated with pledged and 1 other fieldsHigh correlation
country is highly overall correlated with country_0 and 4 other fieldsHigh correlation
usd_pledged is highly overall correlated with pledged and 1 other fieldsHigh correlation
goal_usd is highly overall correlated with goalHigh correlation
month_launched is highly overall correlated with season and 3 other fieldsHigh correlation
season is highly overall correlated with month_launched and 3 other fieldsHigh correlation
category_4 is highly overall correlated with categoryHigh correlation
category_6 is highly overall correlated with categoryHigh correlation
main_category_0 is highly overall correlated with main_categoryHigh correlation
main_category_1 is highly overall correlated with main_categoryHigh correlation
main_category_2 is highly overall correlated with main_categoryHigh correlation
main_category_3 is highly overall correlated with main_categoryHigh correlation
country_0 is highly overall correlated with countryHigh correlation
country_1 is highly overall correlated with countryHigh correlation
country_2 is highly overall correlated with countryHigh correlation
country_3 is highly overall correlated with country and 1 other fieldsHigh correlation
country_4 is highly overall correlated with country and 1 other fieldsHigh correlation
season_0 is highly overall correlated with month_launched and 2 other fieldsHigh correlation
season_1 is highly overall correlated with month_launched and 1 other fieldsHigh correlation
season_2 is highly overall correlated with month_launched and 2 other fieldsHigh correlation
category_0 is highly imbalanced (86.8%)Imbalance
country_0 is highly imbalanced (94.2%)Imbalance
country_1 is highly imbalanced (85.0%)Imbalance
country_2 is highly imbalanced (76.3%)Imbalance
goal is highly skewed (γ1 = 85.66098087)Skewed
pledged is highly skewed (γ1 = 72.83698571)Skewed
backers is highly skewed (γ1 = 84.11949132)Skewed
usd_pledged is highly skewed (γ1 = 81.90561351)Skewed
goal_usd is highly skewed (γ1 = 121.7883294)Skewed
state is uniformly distributedUniform
main_category has 24027 (7.1%) zerosZeros
pledged has 33321 (9.9%) zerosZeros
backers has 33521 (10.0%) zerosZeros
usd_pledged has 37898 (11.3%) zerosZeros
week_day has 53884 (16.0%) zerosZeros

Reproduction

Analysis started2023-11-06 16:05:16.157626
Analysis finished2023-11-06 16:06:24.664866
Duration1 minute and 8.51 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

category
Real number (ℝ)

Distinct158
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.352836
Minimum0
Maximum157
Zeros464
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:24.772835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q141
median85
Q3118
95-th percentile147
Maximum157
Range157
Interquartile range (IQR)77

Descriptive statistics

Standard deviation43.487683
Coefficient of variation (CV)0.54120906
Kurtosis-1.195994
Mean80.352836
Median Absolute Deviation (MAD)38
Skewness-0.046821468
Sum27034069
Variance1891.1786
MonotonicityNot monotonic
2023-11-06T11:06:24.928986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 15461
 
4.6%
39 14195
 
4.2%
128 12224
 
3.6%
89 12041
 
3.6%
58 10042
 
3.0%
135 9617
 
2.9%
55 8469
 
2.5%
147 8085
 
2.4%
54 7991
 
2.4%
52 7504
 
2.2%
Other values (148) 230813
68.6%
ValueCountFrequency (%)
0 464
 
0.1%
1 609
 
0.2%
2 1782
 
0.5%
3 537
 
0.2%
4 245
 
0.1%
5 2054
0.6%
6 624
 
0.2%
7 4815
1.4%
8 4149
1.2%
9 688
 
0.2%
ValueCountFrequency (%)
157 256
 
0.1%
156 573
 
0.2%
155 1751
 
0.5%
154 185
 
0.1%
153 926
 
0.3%
152 4965
1.5%
151 532
 
0.2%
150 3587
1.1%
149 191
 
0.1%
148 812
 
0.2%

main_category
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4351359
Minimum0
Maximum14
Zeros24027
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:25.223467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median7
Q310
95-th percentile13
Maximum14
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8285251
Coefficient of variation (CV)0.51492334
Kurtosis-0.7488814
Mean7.4351359
Median Absolute Deviation (MAD)3
Skewness-0.23527278
Sum2501492
Variance14.657604
MonotonicityNot monotonic
2023-11-06T11:06:25.324429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 59832
17.8%
10 48388
14.4%
12 32561
9.7%
8 27591
8.2%
0 24027
7.1%
7 23875
 
7.1%
4 23863
 
7.1%
13 22756
 
6.8%
5 20026
 
6.0%
11 12186
 
3.6%
Other values (5) 41337
12.3%
ValueCountFrequency (%)
0 24027
7.1%
1 9934
 
3.0%
2 8133
 
2.4%
3 5261
 
1.6%
4 23863
 
7.1%
5 20026
 
6.0%
6 59832
17.8%
7 23875
 
7.1%
8 27591
8.2%
9 7906
 
2.3%
ValueCountFrequency (%)
14 10103
 
3.0%
13 22756
 
6.8%
12 32561
9.7%
11 12186
 
3.6%
10 48388
14.4%
9 7906
 
2.3%
8 27591
8.2%
7 23875
 
7.1%
6 59832
17.8%
5 20026
 
6.0%

goal
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct16830
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36572.435
Minimum0.01
Maximum1 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:25.467376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile350
Q12000
median5000
Q315000
95-th percentile65000
Maximum1 × 108
Range1 × 108
Interquartile range (IQR)13000

Descriptive statistics

Standard deviation955169.65
Coefficient of variation (CV)26.117201
Kurtosis8271.6442
Mean36572.435
Median Absolute Deviation (MAD)4000
Skewness85.660981
Sum1.2304503 × 1010
Variance9.1234906 × 1011
MonotonicityNot monotonic
2023-11-06T11:06:25.627285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 26203
 
7.8%
10000 22003
 
6.5%
1000 15978
 
4.7%
3000 14897
 
4.4%
2000 14608
 
4.3%
15000 12080
 
3.6%
2500 11372
 
3.4%
500 10988
 
3.3%
20000 10647
 
3.2%
1500 10075
 
3.0%
Other values (16820) 187591
55.8%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.15 1
 
< 0.1%
0.5 1
 
< 0.1%
1 348
0.1%
1.065346249 1
 
< 0.1%
1.105931111 1
 
< 0.1%
1.10707411 1
 
< 0.1%
1.192672178 1
 
< 0.1%
1.208031446 1
 
< 0.1%
1.244968427 1
 
< 0.1%
ValueCountFrequency (%)
100000000 20
< 0.1%
99000000 1
 
< 0.1%
80000000 1
 
< 0.1%
73000000 1
 
< 0.1%
70000000 1
 
< 0.1%
60000000 1
 
< 0.1%
58000000 1
 
< 0.1%
55000000 2
 
< 0.1%
50000000 9
< 0.1%
45000000 1
 
< 0.1%

pledged
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct105349
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11398.31
Minimum0
Maximum20338986
Zeros33321
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:25.784842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q198.656585
median1260
Q35638
95-th percentile35000.951
Maximum20338986
Range20338986
Interquartile range (IQR)5539.3434

Descriptive statistics

Standard deviation101818.01
Coefficient of variation (CV)8.932729
Kurtosis9397.944
Mean11398.31
Median Absolute Deviation (MAD)1259
Skewness72.836986
Sum3.8348701 × 109
Variance1.0366908 × 1010
MonotonicityNot monotonic
2023-11-06T11:06:25.946433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33321
 
9.9%
1 6999
 
2.1%
10 3509
 
1.0%
25 2916
 
0.9%
5 2752
 
0.8%
50 2639
 
0.8%
20 2217
 
0.7%
100 2192
 
0.7%
2 1977
 
0.6%
30 1526
 
0.5%
Other values (105339) 276394
82.2%
ValueCountFrequency (%)
0 33321
9.9%
1 6999
 
2.1%
1.01 5
 
< 0.1%
1.02 3
 
< 0.1%
1.03 3
 
< 0.1%
1.04 1
 
< 0.1%
1.045582359 1
 
< 0.1%
1.05 1
 
< 0.1%
1.07 1
 
< 0.1%
1.08 2
 
< 0.1%
ValueCountFrequency (%)
20338986.27 1
< 0.1%
15133210.16 1
< 0.1%
13285226.36 1
< 0.1%
12779843.49 1
< 0.1%
10266845.74 1
< 0.1%
9192055.66 1
< 0.1%
8782571.99 1
< 0.1%
8775872.951 1
< 0.1%
8596474.58 1
< 0.1%
6565782.5 1
< 0.1%

backers
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4152
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.67244
Minimum0
Maximum219382
Zeros33521
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:26.108864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median23
Q378
95-th percentile429
Maximum219382
Range219382
Interquartile range (IQR)75

Descriptive statistics

Standard deviation1092.258
Coefficient of variation (CV)8.1711532
Kurtosis12224.159
Mean133.67244
Median Absolute Deviation (MAD)22
Skewness84.119491
Sum44973023
Variance1193027.5
MonotonicityNot monotonic
2023-11-06T11:06:26.255429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33521
 
10.0%
1 24880
 
7.4%
2 16986
 
5.0%
3 11885
 
3.5%
4 8977
 
2.7%
5 7405
 
2.2%
6 6215
 
1.8%
7 5531
 
1.6%
8 4899
 
1.5%
9 4501
 
1.3%
Other values (4142) 211642
62.9%
ValueCountFrequency (%)
0 33521
10.0%
1 24880
7.4%
2 16986
5.0%
3 11885
 
3.5%
4 8977
 
2.7%
5 7405
 
2.2%
6 6215
 
1.8%
7 5531
 
1.6%
8 4899
 
1.5%
9 4501
 
1.3%
ValueCountFrequency (%)
219382 1
< 0.1%
213767 1
< 0.1%
154926 1
< 0.1%
105857 1
< 0.1%
92950 1
< 0.1%
91585 1
< 0.1%
87142 1
< 0.1%
85581 1
< 0.1%
80523 1
< 0.1%
78471 1
< 0.1%

country
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.588286
Minimum0
Maximum21
Zeros288
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:26.383773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q121
median21
Q321
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.4733844
Coefficient of variation (CV)0.29445343
Kurtosis2.6256892
Mean18.588286
Median Absolute Deviation (MAD)0
Skewness-2.036793
Sum6253880
Variance29.957937
MonotonicityNot monotonic
2023-11-06T11:06:26.493758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 275852
82.0%
9 28205
 
8.4%
3 10954
 
3.3%
1 5741
 
1.7%
5 2268
 
0.7%
16 2016
 
0.6%
8 1778
 
0.5%
12 1547
 
0.5%
7 1257
 
0.4%
19 1254
 
0.4%
Other values (12) 5570
 
1.7%
ValueCountFrequency (%)
0 288
 
0.1%
1 5741
 
1.7%
2 659
 
0.2%
3 10954
 
3.3%
4 447
 
0.1%
5 2268
 
0.7%
6 852
 
0.3%
7 1257
 
0.4%
8 1778
 
0.5%
9 28205
8.4%
ValueCountFrequency (%)
21 275852
82.0%
20 162
 
< 0.1%
19 1254
 
0.4%
18 1161
 
0.3%
17 565
 
0.2%
16 2016
 
0.6%
15 334
 
0.1%
14 115
 
< 0.1%
13 150
 
< 0.1%
12 1547
 
0.5%

usd_pledged
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct139626
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10345.991
Minimum0
Maximum20338986
Zeros37898
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:26.635648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176.99819
median1139.1898
Q35322.819
95-th percentile32273.958
Maximum20338986
Range20338986
Interquartile range (IQR)5245.8208

Descriptive statistics

Standard deviation96564.698
Coefficient of variation (CV)9.333538
Kurtosis11456.317
Mean10345.991
Median Absolute Deviation (MAD)1139.1898
Skewness81.905614
Sum3.4808258 × 109
Variance9.3247409 × 109
MonotonicityNot monotonic
2023-11-06T11:06:26.796345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37898
 
11.3%
1 4618
 
1.4%
25 2650
 
0.8%
10 2650
 
0.8%
50 2264
 
0.7%
5 2011
 
0.6%
100 1918
 
0.6%
20 1692
 
0.5%
2 1301
 
0.4%
30 1257
 
0.4%
Other values (139616) 278183
82.7%
ValueCountFrequency (%)
0 37898
11.3%
0.1474146744 1
 
< 0.1%
0.2493734684 1
 
< 0.1%
0.3455303572 1
 
< 0.1%
0.566314 1
 
< 0.1%
0.57163525 1
 
< 0.1%
0.57361855 1
 
< 0.1%
0.57666895 1
 
< 0.1%
0.57815425 1
 
< 0.1%
0.57935775 1
 
< 0.1%
ValueCountFrequency (%)
20338986.27 1
< 0.1%
15133210.16 1
< 0.1%
13285226.36 1
< 0.1%
12779843.49 1
< 0.1%
10266845.74 1
< 0.1%
9192055.66 1
< 0.1%
8782571.99 1
< 0.1%
8775872.951 1
< 0.1%
8596474.58 1
< 0.1%
6333295.77 1
< 0.1%

goal_usd
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct20372
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48609.338
Minimum0.01
Maximum3.9 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:26.961076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile350
Q11923.0769
median5000
Q315000
95-th percentile75000
Maximum3.9 × 108
Range3.9 × 108
Interquartile range (IQR)13076.923

Descriptive statistics

Standard deviation1387269.3
Coefficient of variation (CV)28.539153
Kurtosis23557.997
Mean48609.338
Median Absolute Deviation (MAD)4000
Skewness121.78833
Sum1.6354223 × 1010
Variance1.9245162 × 1012
MonotonicityNot monotonic
2023-11-06T11:06:27.118405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 22382
 
6.7%
10000 18680
 
5.6%
1000 13026
 
3.9%
3000 12622
 
3.8%
2000 12349
 
3.7%
15000 10187
 
3.0%
2500 9816
 
2.9%
20000 9148
 
2.7%
500 8534
 
2.5%
1500 8343
 
2.5%
Other values (20362) 211355
62.8%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.15 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7692307692 39
< 0.1%
0.8308051343 1
 
< 0.1%
0.8438076498 1
 
< 0.1%
0.8474576271 13
 
< 0.1%
0.8664004035 1
 
< 0.1%
0.8930093443 1
 
< 0.1%
0.9319267935 1
 
< 0.1%
ValueCountFrequency (%)
390000000 1
 
< 0.1%
181818181.8 2
 
< 0.1%
159090909.1 1
 
< 0.1%
138888888.9 1
 
< 0.1%
133333333.3 2
 
< 0.1%
127272727.3 1
 
< 0.1%
109090909.1 1
 
< 0.1%
100000000 14
< 0.1%
92592592.59 1
 
< 0.1%
90909090.91 2
 
< 0.1%

campaigns_duration
Real number (ℝ)

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.567539
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:27.277216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q129
median30
Q335
95-th percentile60
Maximum92
Range91
Interquartile range (IQR)6

Descriptive statistics

Standard deviation12.494211
Coefficient of variation (CV)0.37221112
Kurtosis2.9887027
Mean33.567539
Median Absolute Deviation (MAD)2
Skewness1.3350177
Sum11293530
Variance156.10532
MonotonicityNot monotonic
2023-11-06T11:06:27.426078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 132142
39.3%
60 21554
 
6.4%
29 19809
 
5.9%
45 12814
 
3.8%
31 10669
 
3.2%
35 7981
 
2.4%
40 7549
 
2.2%
28 7118
 
2.1%
32 6352
 
1.9%
20 6156
 
1.8%
Other values (82) 104298
31.0%
ValueCountFrequency (%)
1 122
 
< 0.1%
2 136
 
< 0.1%
3 193
 
0.1%
4 214
 
0.1%
5 431
 
0.1%
6 377
 
0.1%
7 1147
0.3%
8 536
 
0.2%
9 671
 
0.2%
10 1853
0.6%
ValueCountFrequency (%)
92 16
 
< 0.1%
91 437
0.1%
90 795
0.2%
89 824
0.2%
88 186
 
0.1%
87 88
 
< 0.1%
86 83
 
< 0.1%
85 62
 
< 0.1%
84 72
 
< 0.1%
83 53
 
< 0.1%

day_launched
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.164103
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:27.567348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.6001646
Coefficient of variation (CV)0.5671397
Kurtosis-1.1288608
Mean15.164103
Median Absolute Deviation (MAD)7
Skewness0.073650153
Sum5101841
Variance73.962831
MonotonicityNot monotonic
2023-11-06T11:06:27.691585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 12659
 
3.8%
9 12527
 
3.7%
11 12128
 
3.6%
10 12011
 
3.6%
15 11940
 
3.5%
12 11789
 
3.5%
8 11665
 
3.5%
13 11619
 
3.5%
14 11579
 
3.4%
19 11539
 
3.4%
Other values (21) 216986
64.5%
ValueCountFrequency (%)
1 12659
3.8%
2 11171
3.3%
3 11031
3.3%
4 11102
3.3%
5 11016
3.3%
6 11164
3.3%
7 11466
3.4%
8 11665
3.5%
9 12527
3.7%
10 12011
3.6%
ValueCountFrequency (%)
31 5160
1.5%
30 8379
2.5%
29 8866
2.6%
28 9406
2.8%
27 9579
2.8%
26 9650
2.9%
25 10034
3.0%
24 10370
3.1%
23 10552
3.1%
22 10860
3.2%

month_launched
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2883796
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:27.985919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1925521
Coefficient of variation (CV)0.50769075
Kurtosis-1.0874576
Mean6.2883796
Median Absolute Deviation (MAD)3
Skewness0.040945887
Sum2115675
Variance10.192389
MonotonicityNot monotonic
2023-11-06T11:06:28.090975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 34682
10.3%
6 31407
9.3%
5 31305
9.3%
3 31108
9.2%
4 30911
9.2%
8 30519
9.1%
9 29010
8.6%
10 28622
8.5%
2 27167
8.1%
11 23451
7.0%
Other values (2) 38260
11.4%
ValueCountFrequency (%)
1 22901
6.8%
2 27167
8.1%
3 31108
9.2%
4 30911
9.2%
5 31305
9.3%
6 31407
9.3%
7 34682
10.3%
8 30519
9.1%
9 29010
8.6%
10 28622
8.5%
ValueCountFrequency (%)
12 15359
4.6%
11 23451
7.0%
10 28622
8.5%
9 29010
8.6%
8 30519
9.1%
7 34682
10.3%
6 31407
9.3%
5 31305
9.3%
4 30911
9.2%
3 31108
9.2%

year_launched
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.6079
Minimum2009
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:28.198746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2014
Q32015
95-th percentile2016
Maximum2016
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6868276
Coefficient of variation (CV)0.00083771406
Kurtosis-0.73766784
Mean2013.6079
Median Absolute Deviation (MAD)1
Skewness-0.37242585
Sum6.7746225 × 108
Variance2.8453874
MonotonicityNot monotonic
2023-11-06T11:06:28.298945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2015 71963
21.4%
2014 70823
21.1%
2013 53371
15.9%
2012 49345
14.7%
2016 47068
14.0%
2011 30870
9.2%
2010 11647
 
3.5%
2009 1355
 
0.4%
ValueCountFrequency (%)
2009 1355
 
0.4%
2010 11647
 
3.5%
2011 30870
9.2%
2012 49345
14.7%
2013 53371
15.9%
2014 70823
21.1%
2015 71963
21.4%
2016 47068
14.0%
ValueCountFrequency (%)
2016 47068
14.0%
2015 71963
21.4%
2014 70823
21.1%
2013 53371
15.9%
2012 49345
14.7%
2011 30870
9.2%
2010 11647
 
3.5%
2009 1355
 
0.4%

week_day
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3540372
Minimum0
Maximum6
Zeros53884
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-11-06T11:06:28.408937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7097735
Coefficient of variation (CV)0.72631541
Kurtosis-0.82631011
Mean2.3540372
Median Absolute Deviation (MAD)1
Skewness0.34815936
Sum791997
Variance2.9233255
MonotonicityNot monotonic
2023-11-06T11:06:28.502234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 70545
21.0%
2 63682
18.9%
3 56131
16.7%
0 53884
16.0%
4 50410
15.0%
5 26685
 
7.9%
6 15105
 
4.5%
ValueCountFrequency (%)
0 53884
16.0%
1 70545
21.0%
2 63682
18.9%
3 56131
16.7%
4 50410
15.0%
5 26685
 
7.9%
6 15105
 
4.5%
ValueCountFrequency (%)
6 15105
 
4.5%
5 26685
 
7.9%
4 50410
15.0%
3 56131
16.7%
2 63682
18.9%
1 70545
21.0%
0 53884
16.0%

season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
96233 
2
92373 
0
86982 
3
60854 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 96233
28.6%
2 92373
27.5%
0 86982
25.9%
3 60854
18.1%

Length

2023-11-06T11:06:28.617524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:28.753755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 96233
28.6%
2 92373
27.5%
0 86982
25.9%
3 60854
18.1%

Most occurring characters

ValueCountFrequency (%)
1 96233
28.6%
2 92373
27.5%
0 86982
25.9%
3 60854
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 96233
28.6%
2 92373
27.5%
0 86982
25.9%
3 60854
18.1%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 96233
28.6%
2 92373
27.5%
0 86982
25.9%
3 60854
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 96233
28.6%
2 92373
27.5%
0 86982
25.9%
3 60854
18.1%

category_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
330258 
1
 
6184

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 330258
98.2%
1 6184
 
1.8%

Length

2023-11-06T11:06:28.869712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:28.983697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 330258
98.2%
1 6184
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 330258
98.2%
1 6184
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 330258
98.2%
1 6184
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 330258
98.2%
1 6184
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 330258
98.2%
1 6184
 
1.8%

category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
281071 
1
55371 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 281071
83.5%
1 55371
 
16.5%

Length

2023-11-06T11:06:29.078236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:29.201309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 281071
83.5%
1 55371
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 281071
83.5%
1 55371
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 281071
83.5%
1 55371
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 281071
83.5%
1 55371
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 281071
83.5%
1 55371
 
16.5%

category_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
241289 
1
95153 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 241289
71.7%
1 95153
 
28.3%

Length

2023-11-06T11:06:29.311996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:29.445608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 241289
71.7%
1 95153
 
28.3%

Most occurring characters

ValueCountFrequency (%)
0 241289
71.7%
1 95153
 
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 241289
71.7%
1 95153
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 241289
71.7%
1 95153
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 241289
71.7%
1 95153
 
28.3%

category_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
185589 
1
150853 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 185589
55.2%
1 150853
44.8%

Length

2023-11-06T11:06:29.558838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:29.687532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 185589
55.2%
1 150853
44.8%

Most occurring characters

ValueCountFrequency (%)
0 185589
55.2%
1 150853
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 185589
55.2%
1 150853
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 185589
55.2%
1 150853
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 185589
55.2%
1 150853
44.8%

category_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
198234 
1
138208 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 198234
58.9%
1 138208
41.1%

Length

2023-11-06T11:06:29.785470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:29.901809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 198234
58.9%
1 138208
41.1%

Most occurring characters

ValueCountFrequency (%)
0 198234
58.9%
1 138208
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 198234
58.9%
1 138208
41.1%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 198234
58.9%
1 138208
41.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 198234
58.9%
1 138208
41.1%

category_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
184448 
1
151994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 184448
54.8%
1 151994
45.2%

Length

2023-11-06T11:06:30.005106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:30.126736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 184448
54.8%
1 151994
45.2%

Most occurring characters

ValueCountFrequency (%)
0 184448
54.8%
1 151994
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 184448
54.8%
1 151994
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 184448
54.8%
1 151994
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 184448
54.8%
1 151994
45.2%

category_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
181627 
0
154815 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 181627
54.0%
0 154815
46.0%

Length

2023-11-06T11:06:30.251534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:30.381623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 181627
54.0%
0 154815
46.0%

Most occurring characters

ValueCountFrequency (%)
1 181627
54.0%
0 154815
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 181627
54.0%
0 154815
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 181627
54.0%
0 154815
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 181627
54.0%
0 154815
46.0%

category_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
203050 
1
133392 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 203050
60.4%
1 133392
39.6%

Length

2023-11-06T11:06:30.483556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:30.606446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 203050
60.4%
1 133392
39.6%

Most occurring characters

ValueCountFrequency (%)
0 203050
60.4%
1 133392
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 203050
60.4%
1 133392
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 203050
60.4%
1 133392
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 203050
60.4%
1 133392
39.6%

main_category_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
238607 
1
97835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 238607
70.9%
1 97835
29.1%

Length

2023-11-06T11:06:30.706006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:30.842536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 238607
70.9%
1 97835
29.1%

Most occurring characters

ValueCountFrequency (%)
0 238607
70.9%
1 97835
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238607
70.9%
1 97835
29.1%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238607
70.9%
1 97835
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238607
70.9%
1 97835
29.1%

main_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
224582 
1
111860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 224582
66.8%
1 111860
33.2%

Length

2023-11-06T11:06:30.968273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:31.098163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 224582
66.8%
1 111860
33.2%

Most occurring characters

ValueCountFrequency (%)
0 224582
66.8%
1 111860
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 224582
66.8%
1 111860
33.2%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 224582
66.8%
1 111860
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 224582
66.8%
1 111860
33.2%

main_category_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
200391 
0
136051 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 200391
59.6%
0 136051
40.4%

Length

2023-11-06T11:06:31.204718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:31.462758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 200391
59.6%
0 136051
40.4%

Most occurring characters

ValueCountFrequency (%)
1 200391
59.6%
0 136051
40.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 200391
59.6%
0 136051
40.4%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 200391
59.6%
0 136051
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 200391
59.6%
0 136051
40.4%

main_category_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
177855 
0
158587 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 177855
52.9%
0 158587
47.1%

Length

2023-11-06T11:06:31.558660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:31.673998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 177855
52.9%
0 158587
47.1%

Most occurring characters

ValueCountFrequency (%)
1 177855
52.9%
0 158587
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 177855
52.9%
0 158587
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 177855
52.9%
0 158587
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 177855
52.9%
0 158587
47.1%

country_0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
334169 
1
 
2273

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 334169
99.3%
1 2273
 
0.7%

Length

2023-11-06T11:06:31.771388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:31.883590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 334169
99.3%
1 2273
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 334169
99.3%
1 2273
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 334169
99.3%
1 2273
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 334169
99.3%
1 2273
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 334169
99.3%
1 2273
 
0.7%

country_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
329222 
1
 
7220

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 329222
97.9%
1 7220
 
2.1%

Length

2023-11-06T11:06:31.984774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:32.100641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 329222
97.9%
1 7220
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 329222
97.9%
1 7220
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 329222
97.9%
1 7220
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 329222
97.9%
1 7220
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 329222
97.9%
1 7220
 
2.1%

country_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
323359 
1
 
13083

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 323359
96.1%
1 13083
 
3.9%

Length

2023-11-06T11:06:32.195447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:32.307275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 323359
96.1%
1 13083
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 323359
96.1%
1 13083
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 323359
96.1%
1 13083
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 323359
96.1%
1 13083
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 323359
96.1%
1 13083
 
3.9%

country_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
296429 
0
40013 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 296429
88.1%
0 40013
 
11.9%

Length

2023-11-06T11:06:32.403220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:32.522414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 296429
88.1%
0 40013
 
11.9%

Most occurring characters

ValueCountFrequency (%)
1 296429
88.1%
0 40013
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 296429
88.1%
0 40013
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 296429
88.1%
0 40013
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 296429
88.1%
0 40013
 
11.9%

country_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
284191 
1
52251 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 284191
84.5%
1 52251
 
15.5%

Length

2023-11-06T11:06:32.637714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:32.778295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 284191
84.5%
1 52251
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 284191
84.5%
1 52251
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 284191
84.5%
1 52251
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 284191
84.5%
1 52251
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 284191
84.5%
1 52251
 
15.5%

season_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
262141 
1
74301 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 262141
77.9%
1 74301
 
22.1%

Length

2023-11-06T11:06:32.889614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:33.011419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 262141
77.9%
1 74301
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 262141
77.9%
1 74301
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 262141
77.9%
1 74301
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 262141
77.9%
1 74301
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 262141
77.9%
1 74301
 
22.1%

season_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
189075 
1
147367 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 189075
56.2%
1 147367
43.8%

Length

2023-11-06T11:06:33.110809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:33.224384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 189075
56.2%
1 147367
43.8%

Most occurring characters

ValueCountFrequency (%)
0 189075
56.2%
1 147367
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 189075
56.2%
1 147367
43.8%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 189075
56.2%
1 147367
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 189075
56.2%
1 147367
43.8%

season_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
169106 
0
167336 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 169106
50.3%
0 167336
49.7%

Length

2023-11-06T11:06:33.332405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:33.464641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 169106
50.3%
0 167336
49.7%

Most occurring characters

ValueCountFrequency (%)
1 169106
50.3%
0 167336
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 169106
50.3%
0 167336
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 169106
50.3%
0 167336
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 169106
50.3%
0 167336
49.7%

state
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
168221 
0
168221 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters336442
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 168221
50.0%
0 168221
50.0%

Length

2023-11-06T11:06:33.577034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T11:06:33.709436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 168221
50.0%
0 168221
50.0%

Most occurring characters

ValueCountFrequency (%)
1 168221
50.0%
0 168221
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336442
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 168221
50.0%
0 168221
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 336442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 168221
50.0%
0 168221
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 168221
50.0%
0 168221
50.0%

Interactions

2023-11-06T11:06:18.840771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:45.862796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:48.865012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:51.589373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:54.248552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:57.172464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:59.723940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:02.623872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:05.221306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:07.986958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:10.562087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:13.281628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:16.000895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:19.042769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:46.109950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:49.065508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:51.793564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:54.670467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:57.381422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:59.919895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:02.824956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:05.422266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:08.186261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:10.760633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:13.495798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:16.221293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:19.237136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:46.330847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:49.268241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:51.992669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:54.879731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:57.582640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:00.128424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:03.022659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:05.617434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:08.382553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:10.958560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:13.711958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:16.452696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:19.424417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:46.549956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:49.465694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:52.179019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:55.091804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:57.784268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:00.491354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:03.221352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:05.812428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:08.571257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:11.145121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:13.910210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:16.669191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:19.624918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:46.771571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:49.704265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:52.377475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:55.312906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:57.993761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:00.716946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:03.430484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:06.174076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:08.787503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:11.347971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:14.160021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:16.879221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:19.805138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:46.984968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:49.957292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:52.555700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:55.517372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:58.169779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:00.904547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:03.628258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:06.372127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:08.981543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:11.528445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:14.373320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:17.066368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:19.987980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:47.197983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:50.158751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:52.748426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:55.711260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:58.362662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:01.092035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:03.812065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:06.565837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:09.166153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:11.877440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:14.572111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:17.253433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:20.188097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:47.424278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:50.364923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:52.958430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:55.920798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:58.561904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:01.294735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:04.019469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:06.773598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:09.407817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:12.081094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:14.774191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:17.464653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:20.396768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:47.645567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:50.572541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:53.171157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:56.128104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:58.774707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:01.576413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:04.221509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:06.984347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:09.609307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:12.288328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:14.992723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:17.853518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:20.584313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:47.852480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:50.757420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:53.362603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:56.314739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:58.954503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:01.817649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:04.416280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:07.178221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:09.797529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:12.488506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:15.184413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:18.067224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:20.774668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:48.071571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:50.955196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:53.549349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:56.511078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:59.137619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:02.023508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:04.616069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:07.377184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:09.986321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:12.682371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:15.384577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:18.258512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:20.960440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:48.289043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:51.156745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:53.745811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:56.733186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:59.340672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:02.218857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:04.818680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:07.571724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:10.166578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:12.872354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:15.584228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:18.446904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:21.158176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:48.659851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:51.382970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:53.941827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:56.960970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:05:59.533988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:02.442921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:05.025427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:07.774419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:10.368706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:13.084377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:15.805650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-06T11:06:18.648427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-11-06T11:06:33.859164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
categorymain_categorygoalpledgedbackerscountryusd_pledgedgoal_usdcampaigns_durationday_launchedmonth_launchedyear_launchedweek_dayseasoncategory_0category_1category_2category_3category_4category_5category_6category_7main_category_0main_category_1main_category_2main_category_3country_0country_1country_2country_3country_4season_0season_1season_2state
category1.0000.2200.0240.0570.064-0.0100.0580.023-0.0080.001-0.004-0.044-0.0040.0160.0900.3880.4050.2690.5050.3810.5250.3130.3900.3300.3730.4550.0280.0450.0610.0550.0650.0270.0170.0210.167
main_category0.2201.0000.048-0.040-0.041-0.013-0.0390.0470.0310.0000.002-0.0060.0100.0260.1110.2070.2650.3320.2720.3680.3510.3830.8430.6600.6060.7310.0390.0600.0820.0830.0960.0410.0400.0500.144
goal0.0240.0481.0000.2520.1600.0190.2320.9950.2220.0010.0270.107-0.0280.0020.0100.0040.0050.0050.0000.0000.0000.0040.0060.0020.0040.0000.0000.0160.0080.0020.0030.0000.0030.0030.016
pledged0.057-0.0400.2521.0000.9510.0910.9720.2520.021-0.024-0.006-0.110-0.0610.0030.0040.0040.0000.0020.0050.0050.0050.0000.0000.0230.0000.0030.0000.0000.0000.0000.0010.0000.0020.0040.018
backers0.064-0.0410.1600.9511.0000.0780.9340.156-0.004-0.024-0.012-0.114-0.0640.0000.0050.0050.0060.0010.0090.0050.0000.0030.0050.0180.0070.0050.0000.0000.0000.0000.0000.0000.0000.0000.014
country-0.010-0.0130.0190.0910.0781.0000.0980.0200.030-0.005-0.023-0.3100.0080.0250.0330.0390.0750.0240.0400.0310.0180.0570.0660.1200.0660.0510.6210.6260.8840.9680.9650.0390.0320.0210.090
usd_pledged0.058-0.0390.2320.9720.9340.0981.0000.2270.022-0.025-0.035-0.178-0.0550.0020.0040.0030.0030.0020.0040.0030.0060.0000.0010.0210.0000.0050.0000.0000.0000.0000.0000.0000.0000.0030.016
goal_usd0.0230.0470.9950.2520.1560.0200.2271.0000.2200.0000.0270.108-0.0280.0000.0120.0020.0030.0020.0000.0000.0000.0030.0030.0000.0000.0000.0030.0270.0050.0000.0060.0000.0000.0000.012
campaigns_duration-0.0080.0310.2220.021-0.0040.0300.0220.2201.0000.0090.009-0.1130.0160.0390.0200.0380.0340.0340.0530.0400.0240.0490.0300.0790.0700.0560.0150.0190.0350.0540.0570.0180.0600.0300.188
day_launched0.0010.0000.001-0.024-0.024-0.005-0.0250.0000.0091.000-0.035-0.0040.0080.0210.0070.0160.0210.0190.0210.0210.0180.0220.0200.0200.0080.0220.0170.0100.0070.0080.0120.0190.0310.0460.051
month_launched-0.0040.0020.027-0.006-0.012-0.023-0.0350.0270.009-0.0351.000-0.106-0.0160.8530.0130.0190.0230.0220.0200.0270.0250.0270.0130.0450.0310.0400.0230.0280.0330.0120.0220.8670.8560.8840.067
year_launched-0.044-0.0060.107-0.110-0.114-0.310-0.1780.108-0.113-0.004-0.1061.000-0.0300.0740.1130.1710.1440.0710.0270.0830.0530.1020.1110.2570.1740.0840.1210.1480.1910.2280.2750.0700.1220.0740.173
week_day-0.0040.010-0.028-0.061-0.0640.008-0.055-0.0280.0160.008-0.016-0.0301.0000.0270.0060.0270.0290.0220.0270.0210.0210.0240.0130.0270.0250.0110.0060.0130.0180.0160.0190.0150.0400.0180.070
season0.0160.0260.0020.0030.0000.0250.0020.0000.0390.0210.8530.0740.0271.0000.0140.0240.0290.0260.0260.0270.0290.0340.0270.0380.0270.0370.0150.0180.0240.0080.0130.9020.9070.8900.091
category_00.0900.1110.0100.0040.0050.0330.0040.0120.0200.0070.0130.1130.0060.0141.0000.0610.0860.0450.0270.0040.0200.0070.0570.0710.0480.0610.0090.0160.0280.0130.0230.0130.0000.0100.047
category_10.3880.2070.0040.0040.0050.0390.0030.0020.0380.0160.0190.1710.0270.0240.0611.0000.0250.0440.0170.0510.0210.0330.1520.0070.0240.1070.0050.0220.0290.0220.0350.0240.0110.0300.073
category_20.4050.2650.0050.0000.0060.0750.0030.0030.0340.0210.0230.1440.0290.0290.0860.0251.0000.1150.0130.0730.0040.0730.1110.0720.0030.0250.0190.0400.0560.0490.0640.0260.0240.0300.103
category_30.2690.3320.0050.0020.0010.0240.0020.0020.0340.0190.0220.0710.0220.0260.0450.0440.1151.0000.0590.0750.0540.1330.1480.1370.1370.0020.0020.0180.0220.0000.0090.0330.0250.0310.092
category_40.5050.2720.0000.0050.0090.0400.0040.0000.0530.0210.0200.0270.0270.0260.0270.0170.0130.0591.0000.0400.0760.0220.0950.1240.0180.0170.0050.0040.0030.0250.0200.0220.0280.0260.016
category_50.3810.3680.0000.0050.0050.0310.0030.0000.0400.0210.0270.0830.0210.0270.0040.0510.0730.0750.0401.0000.0080.0780.0340.0060.0460.0230.0010.0160.0290.0150.0280.0330.0270.0370.124
category_60.5250.3510.0000.0050.0000.0180.0060.0000.0240.0180.0250.0530.0210.0290.0200.0210.0040.0540.0760.0081.0000.0440.0220.0790.2350.1500.0080.0000.0000.0080.0120.0200.0370.0260.043
category_70.3130.3830.0040.0000.0030.0570.0000.0030.0490.0220.0270.1020.0240.0340.0070.0330.0730.1330.0220.0780.0441.0000.1160.1860.2100.2190.0250.0320.0440.0250.0450.0300.0220.0450.148
main_category_00.3900.8430.0060.0000.0050.0660.0010.0030.0300.0200.0130.1110.0130.0270.0570.1520.1110.1480.0950.0340.0220.1161.0000.0700.2410.1720.0190.0330.0530.0440.0570.0210.0210.0430.095
main_category_10.3300.6600.0020.0230.0180.1200.0210.0000.0790.0200.0450.2570.0270.0380.0710.0070.0720.1370.1240.0060.0790.1860.0701.0000.1840.0750.0400.0670.0950.0540.0810.0480.0000.0260.166
main_category_20.3730.6060.0040.0000.0070.0660.0000.0000.0700.0080.0310.1740.0250.0270.0480.0240.0030.1370.0180.0460.2350.2100.2410.1841.0000.1360.0120.0260.0440.0250.0370.0000.0350.0200.086
main_category_30.4550.7310.0000.0030.0050.0510.0050.0000.0560.0220.0400.0840.0110.0370.0610.1070.0250.0020.0170.0230.1500.2190.1720.0750.1361.0000.0200.0310.0420.0120.0050.0410.0230.0180.113
country_00.0280.0390.0000.0000.0000.6210.0000.0030.0150.0170.0230.1210.0060.0150.0090.0050.0190.0020.0050.0010.0080.0250.0190.0400.0120.0201.0000.0120.0060.1680.0050.0170.0040.0090.024
country_10.0450.0600.0160.0000.0000.6260.0000.0270.0190.0100.0280.1480.0130.0180.0160.0220.0400.0180.0040.0160.0000.0320.0330.0670.0260.0310.0121.0000.3270.0950.1850.0260.0030.0040.058
country_20.0610.0820.0080.0000.0000.8840.0000.0050.0350.0070.0330.1910.0180.0240.0280.0290.0560.0220.0030.0290.0000.0440.0530.0950.0440.0420.0060.3271.0000.2480.3360.0330.0090.0000.086
country_30.0550.0830.0020.0000.0000.9680.0000.0000.0540.0080.0120.2280.0160.0080.0130.0220.0490.0000.0250.0150.0080.0250.0440.0540.0250.0120.1680.0950.2481.0000.7520.0090.0000.0070.026
country_40.0650.0960.0030.0010.0000.9650.0000.0060.0570.0120.0220.2750.0190.0130.0230.0350.0640.0090.0200.0280.0120.0450.0570.0810.0370.0050.0050.1850.3360.7521.0000.0200.0030.0000.064
season_00.0270.0410.0000.0000.0000.0390.0000.0000.0180.0190.8670.0700.0150.9020.0130.0240.0260.0330.0220.0330.0200.0300.0210.0480.0000.0410.0170.0260.0330.0090.0201.0000.4700.5350.061
season_10.0170.0400.0030.0020.0000.0320.0000.0000.0600.0310.8560.1220.0400.9070.0000.0110.0240.0250.0280.0270.0370.0220.0210.0000.0350.0230.0040.0030.0090.0000.0030.4701.0000.0680.058
season_20.0210.0500.0030.0040.0000.0210.0030.0000.0300.0460.8840.0740.0180.8900.0100.0300.0300.0310.0260.0370.0260.0450.0430.0260.0200.0180.0090.0040.0000.0070.0000.5350.0681.0000.083
state0.1670.1440.0160.0180.0140.0900.0160.0120.1880.0510.0670.1730.0700.0910.0470.0730.1030.0920.0160.1240.0430.1480.0950.1660.0860.1130.0240.0580.0860.0260.0640.0610.0580.0831.000

Missing values

2023-11-06T11:06:21.402387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-06T11:06:22.845388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

categorymain_categorygoalpledgedbackerscountryusd_pledgedgoal_usdcampaigns_durationday_launchedmonth_launchedyear_launchedweek_dayseasoncategory_0category_1category_2category_3category_4category_5category_6category_7main_category_0main_category_1main_category_2main_category_3country_0country_1country_2country_3country_4season_0season_1season_2state
0124101.0113.000001321113.0000001.00000030167201532001000000011000100011
16713750.02075.0000036212075.000000750.0000003089201360001000011101000101001
21571220000.021960.000002942121960.00000020000.00000030258201402011111100001000100011
3106142500.02646.500005794100.5577881923.07692330126201542011010001010000010011
475104500.04710.0000040214710.0000004500.00000030157201412001001000011000100011
53962500.02800.0000045212800.0000002500.00000016264201231000101100010000100111
65045000.022492.905951782122492.9059505000.0000003491201403000011001000000100101
7106112200.02812.278663494009.7546861692.30769212291201632001010000001000010101
858725000.033932.530002452133932.53000025000.00000030710201410000001010100000101001
91141220000.047066.000001164973043.69934815384.61538530137201502001010010001000010011
categorymain_categorygoalpledgedbackerscountryusd_pledgedgoal_usdcampaigns_durationday_launchedmonth_launchedyear_launchedweek_dayseasoncategory_0category_1category_2category_3category_4category_5category_6category_7main_category_0main_category_1main_category_2main_category_3country_0country_1country_2country_3country_4season_0season_1season_2state
33643230106000.01346.030211346.0000006000.00000060125201511010100100011000100110
33643330104500.05.01210.0000004500.000000301110201610010100100011000101000
33643415262920.010.012110.0000002920.00000017133201051000100100010000100110
3364355252000.01647.055211647.0000002000.0000004448201252000011101001000100010
3364365610000.01587.048211587.00000010000.00000030293201341010010010010000100110
33643713862000.00.00210.0000002000.000000302310201540100000000010000101000
336438556130000.01571.031211571.000000130000.000000301611201240000111100010000101000
3364393962150.0275.0321275.0000002150.0000002957201002000101100010000100010
3364406542000.01122.042211122.0000002000.00000056238201232010011110111000100010
336441380500.012.0139.585729666.66666729102201513010101111011000110100

Duplicate rows

Most frequently occurring

categorymain_categorygoalpledgedbackerscountryusd_pledgedgoal_usdcampaigns_durationday_launchedmonth_launchedyear_launchedweek_dayseasoncategory_0category_1category_2category_3category_4category_5category_6category_7main_category_0main_category_1main_category_2main_category_3country_0country_1country_2country_3country_4season_0season_1season_2state# duplicates
17813240.00.0090.0184.6153853011720144201010000110100001001010
7558720.00.00210.020.000000301172014420000010101000001000105
111838440.0440.0821440.0440.00000028152014310111000001100001001115
58556500.00.00210.0500.00000090952011010001111000100001001104
6558710.00.00210.010.00000030872014120000010101000001000104
6658710.00.00210.010.00000030972014220000010101000001000104
7358720.00.00210.020.00000030872014120000010101000001000104
7458720.00.00210.020.00000030972014220000010101000001000104
8058750.00.00210.050.00000030972014220000010101000001000104
82587100.00.00210.0100.00000030872014120000010101000001000104